Traditional Destructive Methods Research Articles

Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers.

A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56×56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in Rv 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.

Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it's essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, "greenness") images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.

AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

This study investigates the application of artificial intelligence (AI) to predict the compressive strength of self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing to sustainable construction practices. By leveraging advancements in computational techniques, specifically artificial neural networks (ANNs), we developed highly accurate predictive models to forecast the compressive strength of SCC based on ultrasonic pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved concrete quality, despite the general trend of decreased compressive strength with increased air-entraining admixture (AEA) concentrations. The ANN models show exceptional effectiveness in predicting compressive strength, with a correlation coefficient (R2) of 0.99 between predicted and actual values, providing a robust tool for optimizing SCC mix designs and ensuring quality control. This AI-driven approach enhances sustainability by improving material efficiency and significantly reducing the need for traditional destructive testing methods, thus offering a rapid, reliable, and non-destructive alternative for assessing concrete properties.

Raman spectroscopy as a tool for characterisation of quality parameters in Norwegian grown apples during ripening

This study shows for the first time the feasibility of Raman spectroscopy as a non-destructive method to follow the ripening process of apple fruits. Two different varieties of apples were studied: ‘Aroma’ and ‘Elstar’. By visual inspection, Raman spectra showed that the starch content was higher in ‘Elstar’ apples compared to ‘Aroma’. The degradation of starch over time could be detected in the Raman spectra, indicating that the method can be used to monitor the ripening process. The ripeness markers starch index, soluble solids content (SSC), and the sugars glucose, fructose and sucrose were determined with traditional destructive methods. Cross validated calibration models based on Raman spectroscopy were obtained for all quality parameters, and test set validation offered good results, with R2 in the range 0.4–0.86 for ‘Aroma’ and 0.4–0.95 for ‘Elstar’, respectively. The regression coefficients showed that the calibrations relied on Raman bands associated with starch and different sugars. The results suggest that Raman spectroscopy in the future could be used to determine the optimal time of harvesting and to sort apples into different degrees of ripeness.

A new simple index for characterizing the labile heavy metal concentration in soil by diffusive gradients in thin films technique

Diffusive gradients in thin films technique (DGT) is recognized as a more reliable method for determining labile heavy metal (HM) concentration in soil than traditional destructive methods. However, the current DGT measurement index, CDGT, theoretically underestimates the true labile concentration (Clabile) of HMs in soil and lacks direct comparability with the conventional soil HM content indices due to unit differences. Here, we proposed CDGT-W, a new simple index which is defined as the HM accumulation in the binding layer, normalized to the weight of soil (optimized water content = 100% of the maximum water holding capacity) filled in the open cavity-type DGT device over a specified deployment time (optimized time = 24 h). The procedure for measuring CDGT-W is analogous to that of CDGT but includes precise determination of water content (water/dry soil) and the mass of soil filled in the cavity. We conducted measurements of Cu, Pb, Cr(Ⅵ) and As(V) as CDGT-W, CDGT, solution concentration (Csoln), and CaCl2 extractable concentration (CCaCl2) on three soils with a diverse range of HM concentrations. CDGT-W showed significant linear correlations with all other tested indexes. The ratios of CDGT-W to CCaCl2 varied between 0.30 and 0.98 for all HM–soil combinations with only one exception, a range much greater than CDGT/Csoln (typically <0.1) but lower than 1. This suggested that CDGT-W may more accurately reflect Clabile than CDGT (theoretically underestimates Cliable) and CCaCl2(likely overestimates Cliable). Additionally, CDGT-W measurements for these four HMs exhibited a broad measure concentration range and a low detection limit (mg/kg level). Consequently, CDGT-W may offer a more reliable alternative to CDGT for characterizing Clabile in unsaturated soils.

Gradual error detection technique for non-destructive assessment of density and tensile strength in fused filament fabrication processes

Fused filament fabrication (FFF) is a widely used additive manufacturing process for producing functional components and prototypes. The FFF process involves depositing melted material layer-by-layer to build up 3D physical parts. The quality of the final product depends on several factors, including the component density and tensile strength, which are typically determined through destructive testing methods. X-ray microtomography (XCT) can be used to investigate the pore sizes and distribution. These approaches are time-consuming, costly, and wasteful, making it unsuitable for high-volume manufacturing. In this paper, a new method for non-destructive determination of component density and estimation of the tensile strength in FFF processes is proposed. This method involves the use of gradual error detection by sensors and convolutional neural networks. To validate this approach, a series of experiments has been conducted. Component density and tensile strength of the printed specimens with varying extrusion factor were measured using traditional destructive testing methods and XCT. The cumulative error detection method was used to predict the same properties without destroying the specimens. The predicted values were then compared with the measured values, and it was observed that the method accurately predicted the component density and tensile strength of the tested parts. This approach has several advantages over traditional destructive testing methods. The method is faster, cheaper, and more environmentally friendly since it does not require the destruction of the product. Moreover, it facilitates the testing of each individual part instead of assuming the same properties for components from one series. Additionally, it can provide real-time feedback on the quality of the product during the manufacturing process, allowing for adjustments to be made as needed. The advancement of this approach points toward a future trend in non-destructive testing methodologies, potentially revolutionizing quality assurance processes not only for consumer goods but various industries such as electronics or automotive industry. Moreover, its broader applications extend beyond FFF to encompass other additive manufacturing techniques such as selective laser sintering (SLS), or electron beam melting (EBM). A comparison between the old destructive testing methods and this innovative non-destructive approach underscores the possible fundamental change toward more efficient and sustainable manufacturing practices. This approach has the potential to significantly reduce the time and cost associated with traditional destructive testing methods while ensuring the quality of FFF-manufactured products.

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering.

The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an of 0.721, a of 0.046, and an of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.

Generic above-ground biomass estimator for urban forests using machine learning

ABSTRACT Beyond urban trees’ aesthetic roles in urban landscapes, urban trees have significant environmental and ecological values such as carbon sequestration. In this study, machine learning (ML) namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine Regression (SVR) were used to develop generic AGB estimators for urban trees using the diameter at breast height, total height, and dry wood density of 1051 individual urban trees. The results from the ML were compared with the outputs from a generic allometric equation that was developed using a destructive method. The results showed that the ML represents a good alternative to the traditional destructive method with R2 above 0.9 during training, and R2 above 0.8 during testing. The RF and XGBoost performed better than SVR in the prediction of AGB. However, overall, the AGB predicted using ML was more accurate than the AGB estimated with a generic allometric equation. The generic AGB estimator improves urban forest management by providing an accurate AGB which can support decision-making and can be used for planning, carbon accounting, and monitoring as well as tree species selection and maintenance.

Laser-based ultrasonics for non-destructive material testing: A practical guide

Materials play a crucial role in various industries, ranging from aerospace and automotive to healthcare and electronics. Understanding their mechanical properties is fundamental in ensuring their performance and reliability in different applications. Material testing not only aids in selecting the right material but also in optimizing treatments and processes for enhanced functionality and durability. In recent years, Non-Destructive Testing (NDT) methods have gained prominence due to their ability to assess material properties without causing damage. This approach not only saves time and resources but also allows for continuous monitoring and inspection during the lifespan of a material or component. Among the advanced NDT techniques, Laser-Based Ultrasonics (LBU) has emerged as a powerful tool for non-contact, high-resolution testing and characterization of materials. By harnessing laser technology to generate and detect ultrasonic waves, LBU offers unparalleled sensitivity and precision in assessing material integrity, defects, and structural variations. Its non-invasive nature makes it ideal for inspecting complex geometries, thin films, and delicate components where traditional destructive methods may not be feasible or practical. Furthermore, LBU can provide real-time data, enabling rapid decision-making and quality control in manufacturing processes. This paper aims to highlight the advantages of NDT, particularly the superiority of techniques like LBU, in ensuring the quality, reliability, and performance of materials across diverse industrial sectors. Through comprehensive material testing and characterization, informed decisions can be made regarding material selection, process optimization, and quality assurance, ultimately contributing to advancements in technology and product innovation.

Remote Sensing Monitoring of Rice and Wheat Canopy Nitrogen: A Review

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality.

Lightweight deep CNN models for identifying drought stressed plant

Drought is one of the most severe climatological disasters that has negative impact on agricultural production around the world. Over the years, computer vision technology has been used in conjunction with machine learning applications to replace traditional destructive and time-consuming methods for real-time monitoring of drought-affected plant. Deep learning (DL) techniques have gained a stellar reputation in image classification recently, with convolutional neural network (CNN) emerging as the industry standard. However, the size of deep CNN models is frequently large due to massive number of parameters and field application is often not feasible due to limited storage and computational resources. Several lightweight CNN models have been selected based on the number of network parameters of less than 6M and were trained and tested. The EfficientNet model has achieved a classification accuracy of 88.12 and 88.97 percent for identifying severe drought, mild drought, and no drought plants on visible and near-infrared images respectively. The findings of this study can be used to assist in the development of automated early detection of drought stressed plant with model sizes suitable for real-time plant diagnosis on mobile or embedded devices.

Prediction of Date Fruit Quality Attributes during Cold Storage Based on Their Electrical Properties Using Artificial Neural Networks Models.

Evaluating and predicting date fruit quality during cold storage is critical for ensuring a steady supply of high-quality fruits to meet market demands. The traditional destructive methods take time in the laboratory, and the results are based on one specific parameter being tested. Modern modeling techniques, such as Machine Learning (ML) algorithms, offer unique benefits in nondestructive methods for food safety detection and predicting quality attributes. In addition, the electrical properties of agricultural products provide crucial information about the interior structures of biological tissues and their physicochemical status. Therefore, this study aimed to use an alternative approach to predict physicochemical properties, i.e., the pH, total soluble solids (TSS), water activity (aw), and moisture content (MC) of date fruits (Tamar), during cold storage based on their electrical properties using Artificial Neural Networks (ANNs), which is the most popular ML technique. Ten date fruit cultivars were studied to collect data for the targeted parameters at different cold storage times (0, 2, 4, and 6 months) to train and test the ANNs models. The electrical properties of the date fruits were measured using a high-precision LCR (inductance, capacitance, and resistance) meter from 10 Hz to 100 kHz. The ANNs models were compared with a Multiple Linear Regression (MLR) at all testing frequencies of the electrical properties. The MLR models were less accurate than ANNs models in predicting fruit pH and had low performance and weak predictive ability for the TSS, aw, and MC at all testing frequencies. The optimal ANNs prediction model consisted of the input layer with 14 neurons, one hidden layer with 15 neurons, and the output layer with 4 neurons, which was determined depending on the measurements of the electrical properties at a 10 kHz testing frequency. This optimal ANNs model was able to predict the pH with R2 = 0.938 and RMSE = 0.121, TSS with R2 = 0.954 and RMSE = 2.946, aw with R2 = 0.876 and RMSE = 0.020, and MC with R2 = 0.855 and RMSE = 0.803 b by using the measured electrical properties. The developed ANNs model is a powerful tool for predicting fruit quality attributes after learning from the experimental measurement parameters. It can be suggested to efficiently predict the pH, total soluble solids, water activity, and moisture content of date fruits based on their electrical properties at 10 kHz.

A non-destructive method for rapid acquisition of grassland aboveground biomass for satellite ground verification using UAV RGB images

Remote sensing has become an indispensable method for estimating the regional-scale collection of grassland aboveground biomass (AGB). However, the lack of ground verification samples often reduces the inversion accuracy. This paper aimed to find a non-destructive method to quickly obtain grassland AGB at quadrat-scale through unmanned aerial vehicles (UAVs) in a large area. Thus, we proposed and assessed the vertical and horizontal indices from UAV RGB images as predictors of grassland AGB using the random forest (RF) machine learning technique. By comparing the performance of different indices combinations, we found that the model combing the horizontal and vertical indices (RF VH ) performed best (R 2 = 0.78; RMSE = 24.80 g/m 2 ), followed by the model using only horizontal indices (the RF H model; R 2 =0.73; RMSE =26.54 g/m 2 ), and the last was the model using only the vertical index. However, the RF VH model was unsuitable for collecting AGB samples in a large area because the UAVs with RGB cameras failed to obtain vegetation height information in areas with high vegetation coverage. In conclusion, the RF H model can be used to replace the traditional destructive method for collecting ground data over large regions for AGB satellite inversion.

Efficacy of MMR Vaccine as Intralesional Immunotherapy in Viral Warts

Background: Warts are common in humans and account for 8% of visits to dermatologists. There are many destructive therapies available for the treatment of common warts, and no single treatment has yet proven 100% effective. Material and Method: In this hospital-based prospective study, a total of 100 patients with viral warts excluding genital warts were selected. Detailed history was recorded, especially the type of wart, duration, site and history of any treatment taken in the past. Clinical photo-graphs were taken at the baseline and every three weeks for assessment. The MMR vaccine was given at various intervals, and the patients were follow-ups to observed recurrences. Result: Most common type of warts were Verruca Vulgaris and palmoplantar warts observed in 37.38% patients each. Periungual warts were seen in 14.95% subjects and 10.28% patients had verruca plana. Majority of the subjects were males (68.22%) and 31.78% were females. Majority of the patients had warts on the face followed by palms and soles. Majority of our patients (44.86%) had warts from 6-12 months. Most, i.e. 66.36% of patients belonged to the 21- 40 years age group. Multiple warts were seen in 68.22% of patients and single warts in 31.78% of patients. Complete response was observed in 76.64% of patients, partial response in 14.95% of patients and no response in 8.41% of patients. Recurrence was noted in 2 of 107 patients. No statistically significant difference was seen in the different types and distribution of warts. Conclusion: Treatment of warts using the intralesional MMR technique appears better tolerated and a safe option than traditional destructive methods. As seen earlier, we report the occurrence of warts to be more predominant in the younger generation, with a higher incidence in males. The complete response with no recurrence after a 6-month follow-up demonstrates the efficacy of the treatment

IETeasy: An open source and low-cost instrument for impulse excitation technique, applied to materials classification by acoustical and mechanical properties assessment.

In the past twenty years, impulse excitation technique (IET) has become a widely diffused non-destructive technique in metal industry field. This success resides in its capability to determine with high precision and accuracy some elastic properties of materials, such as Young’s modulus, shear modulus and Poisson’s ratio. The technique, which is very fast and non-destructive, consists in exciting a sample by a mechanical input and registering the acoustic output that, once analyzed by Fast Fourier-Transformation (FFT), provides the resonant frequencies of the sample, with a fast data analysis procedure. The approach is thus very easy to be applied to most materials and cost and time effective. Despite these many advantages, IET is still an under exploited technique in academic research centres, that mainly rely on traditional destructive methods for the evaluation of such properties, for instance by the measurement of strain-stress curves. Commercial IET instruments, similarly to traditional ones, have costs spanning from many hundreds to thousands of dollars, limiting their diffusion in academic world but also in small companies with limited R&D or quality control expenses. Non-professional instruments can also give very precise results and can be successfully used in basic research and in quality control even if not certified as commercial ones. Moreover they can be easily customized according to specific user needs and sample features. Since no examples of low cost IET designs can still be found in the scientific literature, we fill the gap in this paper, giving instructions for a self-assembled instrument for IET analysis, with a cost in the range of 70–85 USD. Moreover, the collected calibration data are analyzed to prove that the instrument can be used for other purposes than the common elastic properties determination, but also for a fast and cheap material characterization exploiting a multivariate analysis approach. Calibration results show that IETeasy can be used in both academic and industrial field for quality control purposes as a low-cost, fast and efficient alternative to tensometers. Principal component analysis, applied in this paper for the first time to IET data analysis, was able to distinguish and classify steel from Al or Cu alloys from polymers, but also different steel grades, demonstrating its potential in massive and eventually automatic IET data analysis. Calculated mechanical properties fitted with good approximation the ranges expected for each sample.

Efficacy of viable BCG vaccine paste in the treatment of common warts: a double-blind randomized control trial

Background Treatment of common warts may be painful or leaves scars, mainly using traditional destructive methods. This study aimed to evaluate the efficacy of the viable Bacillus Calmette‐Guérin (BCG) vaccine in paste formula as an immunotherapeutic modality for common warts treatment. Methods This double-blind and randomized, parallel-group, placebo-controlled trial was conducted at the Ahvaz Imam Hospital Dermatology Department from November 2014 to 2015. Overall 80 patients with common warts in two groups (case and control) received BCG vaccine paste once weekly for eight consecutive weeks. Follow-up was done every two weeks during treatment and six months after the treatment to evaluate recurrence in patients with complete resolution. Results In group A, eight patients (20%) had a complete response, 15 patients (37.5%) partial response, four patients (10%) low response, and 13 patients (32.5%) no response (p < .001). All patients in group B had no response to treatment (p < .001). After six months of follow-up, no recurrence was seen. Duration of disease less than 12 months (p = .001) and the number of lesions less than three (p = .01) were determining factors of response to treatment. Conclusion Topical BCG vaccine paste was an effective treatment for common warts, without recurrence and significant complications.

Applying innovative technologies for road surfaces monitoring

The paper considers the advantages and nature of georadar monitoring of road surfaces comparing to traditional destructive methods of road exploration. The authors checked the thickness of the structural layers of pavement to determine appropriate repair work using the amplitude-frequency characteristics and the dielectric constant (the speed of signal transmission). The article examined the section of the highway and the examination data was recorded in a file, which allows further study and documentation of the material with the help of computer equipment.

Efficacy of intralesional MMR vaccine for the management of cutaneous warts in adults

Background: No single treatment for warts has proven 100% efficacy and most therapeutic modalities remain unsatisfactory. Immunotherapy with Measles-Mumps-Rubella (MMR) vaccine remains a key treatment of interest. The study done from August 2020 to March 2021 in the department of Dermatology, KIMS hospital, Narketpally. Telangana state.Objective: To evaluate the efficacy of intralesional MMR vaccine in the treatment of cutaneous warts in adults. Patients and Methods: Fifty patients (34 men and 16 women) aged 18–61 (mean ± standard deviation = 34.58 ± 11.74) years with common warts received 0.25 ml of MMR vaccine injected intralesionally in the largest wart. The dose was repeated at 2-week interval until complete clearance or for a maximum of 5 doses. Thereafter, they were followed up once a month for 24-week study period. The response was evaluated as complete clearance (complete disappearance of the wart(s) including distant ones and appearance of normal skin), partial clearance (≤99% reduction in size and number including distant ones and few residual warts still visible), good response (some reduction in size only including that of distant ones but no decrease in number of warts), or poor response (no change in size and number). Results: 50 patients completed the study and 29 (58%) of them had complete clearance of warts, 14 (28%) showed partial clearance and 5 (10%) patients showed good response. Complete clearance of warts occurred after five doses in 19 (38%) patients and after 4 doses in 9 (18%) patients. Conclusion: MMR vaccine is a promising treatment modality for common warts, particularly the multiple and recalcitrant ones. It seems to be inexpensive, effective and safe option that has the potential advantages of widespread and sustained effects against HPV. Intralesional MMR also appears to be much less painful and safe than traditional destructive methods for wart treatment, and thus seems to be better tolerated.

In vivo tracing of endogenous salicylic acids as the biomarkers for evaluating the toxicity of nano-TiO2 to plants

Currently, nano-titanium dioxide (nTiO2) is considered an emerging environmental contaminant. Bottlenecked by the traditional destructive and lethal sampling methods, nTiO2’s effect in living plants is poorly investigated. Here, in vivo tracing of endogenous salicylic acids at regular intervals was performed by using solid phase microextraction (SPME) technique for evaluating the effects of nTiO2 on plants. By planting aloe in soil containing varying amounts of nTiO2, the titanium (Ti) element accumulated in the leaves to concentrations and then reached the maximum of 1.1 ± 0.4 μg/g after nTiO2 exceeding 0.1 g/kg. The levels of salicylic acid (SA) and acetylsalicylic acid (ASA) were up-regulated upon the exposure to nTiO2, while were positively correlated to the contents of Ti. Moreover, the increased malondialdehyde, decreased total superoxide dismutase and fluctuated glutathione along with the addition of nTiO2 demonstrated the oxidative stress caused by nTiO2. Meanwhile, apparent growth indicators including leaf elongation, plant fresh weight and root development were influenced, which further confirmed the toxicity of nTiO2 imparted on aloe. This study presents the possibility of using salicylic acids as biomarkers for revealing the toxicity of nTiO2 on plants in addition to the other biomarkers and biomass data, and the in vivo SPME technique is powerful for their monitoring.

Method to test the durability of fiber-reinforced polymers based on elastic modulus

The performance degradation of fiber-reinforced polymer is often assessed based on the ultimate strength obtained during a destructive test. This method has the advantages of simple operation, clear internal mechanism, and noticeable data variation. However, because fiber-reinforced polymers are composed of high molecular compounds, their properties are influenced by several factors, such as the manufacturing process and composition ratio. Without a unified benchmark, it is difficult to unify destructive test data obtained by different researchers and organizations. In this study, a new test method named SNFT (Same Non-failure FRP Test) is proposed for assessing the durability of fiber-reinforced polymers. SNFT is a testing method in which the same specimen can be repeatedly tested at different aging periods. The rationality of the SNFT method is analyzed using the displacement compatibility between the degraded zone and the undegraded zone. The conversion principle based on the degraded degree between the destructive test and the SNFT method is deduced by calculation. An experimental test was designed to investigate the test conditions and control parameters of the SNFT method. SNFT and synchronous destructive tests on glass fiber–reinforced plastic were carried out under the conditions of wet heat and alkali solution, and the theoretical results were compared and verified with data in the literature. The results show that compared with the traditional durability destructive test method, the SNFT method shows more consistent data and less data fluctuation and incurs a lower test cost. The elastic modulus, adopted as the durability benchmark, can be similar to the test results of traditional destructive test methods and could be transformed based on the transformation relationship proposed in this article. The degradation of fiber-reinforced polymer in different environments is characterized by the variation in elastic modulus measured using the SNFT method. This study provides a theoretical basis for establishing a unified benchmark of durability tests and data and supports the quantitative design of durability.